Optical coherence tomography (“OCT”) is an imaging technique that can measure an interference between a reference beam of light and a detected beam reflected back from a sample. A detailed system description of convention time-domain OCT has been provided in Huang et al. “Optical coherence tomography,” Science 254 (5035), 1178-81 (1991). The spectral-domain variant of optical coherence tomography (“OCT”), called spectral-domain optical coherence tomography (“SD-OCT”), is a technique is a technology that is suitable for ultrahigh-resolution ophthalmic imaging. This technique has been described in Cense, B. et al., “Ultrahigh-resolution high-speed retinal imaging using spectral-domain optical coherence tomography”, Optics Express, 2004 and in International Patent Publication No. WO 03/06280 In addition, U.S. patent application Ser. No. 10/272,171 filed on Oct. 16, 2002, Wojtkowski et al., “In Vivo Human Retinal imaging by Fourier Domain Optical Coherence Tomography”, Journal of Biomedical Optics, 2002, 7(3), pp. 457-463, Nassif, N. et al., “In Vivo Human Retinal Imaging by Ultrahigh-Speed Spectral Domain Optical Coherence Tomography,” Optics Letters, 2004, 29(5), pp. 480-482, also relates to this subject matter. In addition, optical frequency domain interferometry (“OFDI”) setup (as described in Yun, S. H. et al., “High-Speed Optical Frequency-Domain Imaging, Optics Express, 2003, 11(22), pp. 2953-2963, International Publication No. WO 03/062802 and U.S. Patent Application Ser. No. 60/514,769 filed on Oct. 27, 2004 further relate to the subject matter of the present invention.
The imaging range (e.g., a depth of the image), in SD-OCT and OFDI are generally fixed by parameters of a spectrometer. The imaging range in conventional time-domain OCT systems can be determined by the magnitude of the sweep in a reference arm length. In such systems, the overall reference arm length generally determines the position of the imaging region of a sample. By increasing the reference arm length or by moving a reference arm sweep to deeper lengths, the imaging region may be made deeper, while reducing the reference arm length can moves the imaging region to a more shallow area of the sample.
These technologies have been successfully applied to imaging biological sample. However, such biological samples may often contain irregular surfaces and structures that can make imaging problematic. For example, a curved topology of a retina generally indicates that retinal surface may appear at one depth for a particular scan, while appearing at a different depth for a scan at a different lateral location. In addition, a motion of the sample may further compounds this problem. One of the advantages of the above-referenced imaging techniques and systems employing such techniques is that they do not contact the sample and that they are non-invasive. However, this means that it is often impossible to eliminate or significantly reduce the motion of the sample relative to the imaging device. Referring to the example of retinal imaging, any slight motion on the part of the subject whose retina is being imaged would likely result in undesirable variations in position of the entire eye, in addition to the topological variations inherent in the eye itself. It should be understood that techniques to stabilize and account for motion and topological variations may significantly facilitate the application of these imaging technologies by addressing the motion problem described above.
One possible approach to address for these variations may be to increase the imaging range so as to accommodate these variations due to motion or topology. Again, using the retinal sample as an example, if the range in position of the retinal surface is 10 mm, it is possible to use a system which provides an overall imaging depth of 12 mm. With such system, the consideration for the movement of the surface from image to image are not essential since the retina would likely always be within the proper range. However, using this approach may have the effect of degrading the signal-to-noise ratio and sensitivity of the image.
Accordingly, a method to track the location of features within the sample for the purpose of determining the most appropriate imaging position and range is likely desirable. Previous techniques typically used the position of the surface of the sample as determined from a structural (intensity) image (e.g., using a cross-correlation technique or a peak signal), and provided to adapt a ranging location. (See U.S. Pat. Nos. 6,191,862 and 6,552,796). However, the detection of such prior techniques was not robust.